High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

MSP-MVS		90.38 3	91.87 1	85.88 82	92.83 76	64.03 187	93.06 100	94.33 48	82.19 19	93.65 2	96.15 27	85.89 1	97.19 79	91.02 15	97.75 1	96.43 22
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
OPU-MVS				89.97 3	97.52 3	73.15 11	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

DPM-MVS		90.70 2	90.52 5	91.24 1	89.68 151	76.68 2	97.29 1	95.35 10	82.87 14	91.58 8	97.22 4	79.93 3	99.10 7	83.12 74	97.64 2	97.94 1

baseline2		83.68 87	83.42 77	84.48 131	87.37 202	66.00 131	90.06 214	95.93 7	79.71 45	69.08 209	90.39 154	77.92 4	96.28 116	78.91 109	81.38 155	91.16 180

GG-mvs-BLEND				86.53 62	91.91 103	69.67 41	75.02 330	94.75 28		78.67 113	90.85 145	77.91 5	94.56 180	72.25 154	93.74 43	95.36 50

gg-mvs-nofinetune		77.18 193	74.31 210	85.80 87	91.42 119	68.36 65	71.78 332	94.72 29	49.61 334	77.12 127	45.92 350	77.41 6	93.98 206	67.62 195	93.16 52	95.05 70

SED-MVS		89.94 7	90.36 7	88.70 13	96.45 11	69.38 43	96.89 4	94.44 40	71.65 187	92.11 3	97.21 5	76.79 7	99.11 4	92.34 6	95.36 12	97.62 2

test_241102_ONE					96.45 11	69.38 43		94.44 40	71.65 187	92.11 3	97.05 8	76.79 7	99.11 4

test_0728_THIRD									72.48 157	90.55 12	96.93 10	76.24 9	99.08 9	91.53 12	94.99 15	96.43 22

DPE-MVS		88.77 13	89.21 13	87.45 33	96.26 18	67.56 86	94.17 52	94.15 53	68.77 234	90.74 11	97.27 3	76.09 10	98.49 23	90.58 17	94.91 18	96.30 25
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + GP.		87.96 17	88.37 16	86.70 55	93.51 60	65.32 148	95.15 34	93.84 59	78.17 68	85.93 38	94.80 67	75.80 11	98.21 31	89.38 21	88.78 98	96.59 13

DeepPCF-MVS	81.17 1	89.72 8	91.38 3	84.72 123	93.00 73	58.16 280	96.72 7	94.41 43	86.50 5	90.25 14	97.83 1	75.46 12	98.67 19	92.78 3	95.49 11	97.32 4

MVSTER		82.47 102	82.05 99	83.74 145	92.68 83	69.01 51	91.90 146	93.21 88	79.83 41	72.14 175	85.71 210	74.72 13	94.72 174	75.72 129	72.49 215	87.50 221

test_241102_TWO								94.41 43	71.65 187	92.07 5	97.21 5	74.58 14	99.11 4	92.34 6	95.36 12	96.59 13

ETH3 D test6400		90.27 5	90.44 6	89.75 6	96.82 6	74.33 7	95.89 16	94.80 26	77.13 80	89.13 18	97.38 2	74.49 15	98.48 24	92.32 9	95.98 6	96.46 21

DELS-MVS		90.05 6	90.09 8	89.94 4	93.14 70	73.88 8	97.01 3	94.40 45	88.32 2	85.71 41	94.91 63	74.11 16	98.91 13	87.26 46	95.94 7	97.03 8
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
DVP-MVS		89.41 10	89.73 11	88.45 18	96.40 14	69.99 31	96.64 8	94.52 36	71.92 173	90.55 12	96.93 10	73.77 17	99.08 9	91.91 10	94.90 19	96.29 26
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					96.40 14	69.99 31	96.76 6	94.33 48	71.92 173	91.89 6	97.11 7	73.77 17

ET-MVSNet_ETH3D		84.01 78	83.15 84	86.58 59	90.78 134	70.89 21	94.74 45	94.62 34	81.44 29	58.19 296	93.64 96	73.64 19	92.35 257	82.66 78	78.66 174	96.50 19

CSCG		86.87 35	86.26 41	88.72 12	95.05 30	70.79 22	93.83 77	95.33 11	68.48 238	77.63 120	94.35 81	73.04 20	98.45 25	84.92 62	93.71 45	96.92 9

tttt0517		79.50 152	78.53 151	82.41 175	87.22 204	61.43 236	89.75 223	94.76 27	69.29 226	67.91 227	88.06 184	72.92 21	95.63 144	62.91 236	73.90 206	90.16 190

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 39	96.26 24	72.84 22	99.38 1	92.64 4	95.93 8	97.08 7

thisisatest0515		83.41 88	82.49 95	86.16 75	89.46 157	68.26 69	93.54 86	94.70 30	74.31 116	75.75 136	90.92 143	72.62 23	96.52 113	69.64 175	81.50 154	93.71 120

thisisatest0530		81.15 121	80.07 124	84.39 133	88.26 183	65.63 142	91.40 167	94.62 34	71.27 200	70.93 187	89.18 167	72.47 24	96.04 128	65.62 217	76.89 192	91.49 170

TSAR-MVS + MP.		88.11 16	88.64 14	86.54 61	91.73 109	68.04 74	90.36 206	93.55 74	82.89 13	91.29 9	92.89 113	72.27 25	96.03 129	87.99 35	94.77 23	95.54 46
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EPP-MVSNet		81.79 115	81.52 106	82.61 169	88.77 172	60.21 257	93.02 104	93.66 70	68.52 237	72.90 162	90.39 154	72.19 26	94.96 166	74.93 137	79.29 168	92.67 148

CostFormer		82.33 104	81.15 109	85.86 85	89.01 167	68.46 63	82.39 296	93.01 99	75.59 96	80.25 90	81.57 255	72.03 27	94.96 166	79.06 107	77.48 186	94.16 102

HPM-MVS++		89.37 11	89.95 10	87.64 26	95.10 29	68.23 71	95.24 31	94.49 38	82.43 17	88.90 20	96.35 21	71.89 28	98.63 20	88.76 30	96.40 4	96.06 31

CNVR-MVS		90.32 4	90.89 4	88.61 16	96.76 7	70.65 23	96.47 12	94.83 23	84.83 8	89.07 19	96.80 12	70.86 29	99.06 11	92.64 4	95.71 9	96.12 30

DWT-MVSNet_test		83.95 80	82.80 89	87.41 34	92.90 75	70.07 30	89.12 237	94.42 42	82.15 20	77.64 119	91.77 135	70.81 30	96.22 118	65.03 223	81.36 156	95.94 35

IB-MVS	77.80 4	82.18 106	80.46 123	87.35 36	89.14 164	70.28 28	95.59 25	95.17 14	78.85 58	70.19 196	85.82 208	70.66 31	97.67 51	72.19 157	66.52 254	94.09 106
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
baseline1		81.84 114	81.03 114	84.28 137	91.60 112	66.62 116	91.08 185	91.66 152	81.87 24	74.86 145	91.67 139	69.98 32	94.92 169	71.76 161	64.75 267	91.29 179

agg_prior1		87.02 33	87.26 30	86.28 73	94.16 44	66.97 106	94.08 59	93.31 85	71.85 180	84.49 52	95.39 40	68.91 33	96.75 106	88.84 29	94.32 34	95.13 66

alignmvs		87.28 26	86.97 34	88.24 20	91.30 122	71.14 20	95.61 24	93.56 73	79.30 49	87.07 29	95.25 49	68.43 34	96.93 100	87.87 36	84.33 137	96.65 11

PAPM		85.89 50	85.46 53	87.18 39	88.20 186	72.42 12	92.41 127	92.77 107	82.11 21	80.34 89	93.07 107	68.27 35	95.02 164	78.39 114	93.59 47	94.09 106

train_agg		87.21 28	87.42 28	86.60 57	94.18 40	67.28 95	94.16 53	93.51 75	71.87 178	85.52 43	95.33 42	68.19 36	97.27 77	89.09 25	94.90 19	95.25 63

test_8					94.19 39	67.19 98	94.15 55	93.42 82	71.87 178	85.38 45	95.35 41	68.19 36	96.95 97

ETH3D cwj APD-0.16		87.06 31	87.18 32	86.71 53	91.99 99	67.48 91	92.97 105	94.21 51	71.48 198	85.72 40	96.32 23	68.13 38	98.00 38	89.06 26	94.70 27	94.65 84

TEST9					94.18 40	67.28 95	94.16 53	93.51 75	71.75 185	85.52 43	95.33 42	68.01 39	97.27 77

test_prior3		87.38 25	87.70 22	86.42 66	94.71 33	67.35 93	95.10 36	93.10 97	75.40 101	85.25 48	95.61 37	67.94 40	96.84 102	87.47 41	94.77 23	95.05 70

test_prior2							95.10 36		75.40 101	85.25 48	95.61 37	67.94 40		87.47 41	94.77 23

WTY-MVS		86.32 43	85.81 48	87.85 22	92.82 78	69.37 45	95.20 32	95.25 12	82.71 15	81.91 72	94.73 68	67.93 42	97.63 56	79.55 102	82.25 148	96.54 16

Regformer-1		87.24 27	87.60 25	86.15 76	95.14 27	65.83 137	93.95 66	95.12 15	82.11 21	84.25 54	95.73 33	67.88 43	98.35 29	85.60 56	88.64 100	94.26 96

ETH3D-3000-0.1		87.61 22	87.89 19	86.75 52	93.58 57	67.21 97	94.31 50	94.14 54	72.92 148	87.13 26	96.62 14	67.81 44	97.94 39	90.13 18	94.42 32	95.09 68

APDe-MVS		87.54 23	87.84 20	86.65 56	96.07 21	66.30 124	94.84 44	93.78 60	69.35 225	88.39 21	96.34 22	67.74 45	97.66 54	90.62 16	93.44 49	96.01 34

tpm2		79.80 147	77.95 160	85.34 105	88.28 182	68.26 69	81.56 301	91.42 161	70.11 216	77.59 122	80.50 273	67.40 46	94.26 193	67.34 197	77.35 187	93.51 125

Regformer-2		87.00 34	87.43 27	85.71 92	95.14 27	64.73 166	93.95 66	94.95 20	81.69 26	84.03 60	95.73 33	67.35 47	98.19 33	85.40 58	88.64 100	94.20 98

miper_enhance_ethall		78.86 162	77.97 159	81.54 199	88.00 191	65.17 152	91.41 165	89.15 244	75.19 106	68.79 216	83.98 227	67.17 48	92.82 235	72.73 149	65.30 258	86.62 241

xxxxxxxxxxxxxcwj		87.14 29	87.19 31	86.99 45	93.84 49	67.89 78	95.05 38	84.72 305	78.19 66	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

SF-MVS		87.03 32	87.09 33	86.84 47	92.70 82	67.45 92	93.64 81	93.76 63	70.78 210	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

HY-MVS	76.49 5	84.28 71	83.36 80	87.02 44	92.22 92	67.74 82	84.65 278	94.50 37	79.15 53	82.23 70	87.93 185	66.88 51	96.94 98	80.53 97	82.20 149	96.39 24

RRT_test8_iter05		80.61 130	79.62 133	83.60 152	91.87 107	66.90 108	93.42 93	93.68 68	77.09 82	68.83 215	85.63 211	66.82 52	95.42 155	76.46 127	62.74 281	88.48 209

EPNet		87.84 20	88.38 15	86.23 74	93.30 63	66.05 129	95.26 30	94.84 22	87.09 3	88.06 22	94.53 72	66.79 53	97.34 70	83.89 70	91.68 73	95.29 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
9.14			87.63 23		93.86 48		94.41 48	94.18 52	72.76 151	86.21 33	96.51 16	66.64 54	97.88 45	90.08 19	94.04 37

FIs		79.47 153	79.41 139	79.67 243	85.95 224	59.40 267	91.68 158	93.94 57	78.06 69	68.96 212	88.28 177	66.61 55	91.77 269	66.20 211	74.99 199	87.82 218

NCCC		89.07 12	89.46 12	87.91 21	96.60 9	69.05 50	96.38 13	94.64 33	84.42 9	86.74 30	96.20 25	66.56 56	98.76 18	89.03 28	94.56 29	95.92 37

SD-MVS		87.49 24	87.49 26	87.50 32	93.60 56	68.82 56	93.90 70	92.63 115	76.86 84	87.90 23	95.76 32	66.17 57	97.63 56	89.06 26	91.48 77	96.05 32
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
UniMVSNet_NR-MVSNet		78.15 178	77.55 167	79.98 234	84.46 248	60.26 255	92.25 129	93.20 90	77.50 77	68.88 213	86.61 199	66.10 58	92.13 261	66.38 208	62.55 282	87.54 220

CHOSEN 280x420		77.35 191	76.95 178	78.55 258	87.07 207	62.68 219	69.71 337	82.95 321	68.80 233	71.48 184	87.27 196	66.03 59	84.00 330	76.47 126	82.81 146	88.95 201

CANet		89.61 9	89.99 9	88.46 17	94.39 38	69.71 39	96.53 11	93.78 60	86.89 4	89.68 15	95.78 31	65.94 60	99.10 7	92.99 2	93.91 40	96.58 15

segment_acmp												65.94 60

Regformer-3		85.80 51	85.92 46	85.46 99	94.17 42	65.09 159	92.95 107	95.11 16	81.13 32	81.68 74	95.04 54	65.82 62	98.32 30	83.02 75	84.36 134	92.97 142

Vis-MVSNet (Re-imp)		79.24 156	79.57 134	78.24 263	88.46 177	52.29 317	90.41 204	89.12 246	74.24 118	69.13 207	91.91 133	65.77 63	90.09 293	59.00 259	88.09 105	92.33 156

FC-MVSNet-test		77.99 180	78.08 157	77.70 266	84.89 241	55.51 303	90.27 208	93.75 66	76.87 83	66.80 243	87.59 189	65.71 64	90.23 290	62.89 237	73.94 204	87.37 225

SMA-MVS		88.14 14	88.29 17	87.67 25	93.21 67	68.72 58	93.85 73	94.03 56	74.18 119	91.74 7	96.67 13	65.61 65	98.42 28	89.24 24	96.08 5	95.88 39
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
test12				87.09 42	94.60 35	68.86 54		92.91 103		82.67 69		65.44 66	97.55 59		93.69 46	94.84 77

旧先验1					91.94 100	60.74 248		91.50 158			94.36 77	65.23 67			91.84 70	94.55 86

Regformer-4		85.45 54	85.69 51	84.73 121	94.17 42	63.23 204	92.95 107	94.83 23	80.66 36	81.29 77	95.04 54	65.12 68	98.08 36	82.74 77	84.36 134	92.88 146

1112_ss		80.56 132	79.83 130	82.77 164	88.65 173	60.78 245	92.29 128	88.36 268	72.58 154	72.46 171	94.95 58	65.09 69	93.42 221	66.38 208	77.71 179	94.10 105

MVSFormer		83.75 85	82.88 87	86.37 69	89.24 162	71.18 18	89.07 238	90.69 186	65.80 256	87.13 26	94.34 82	64.99 70	92.67 243	72.83 147	91.80 71	95.27 60

lupinMVS		87.74 21	87.77 21	87.63 30	89.24 162	71.18 18	96.57 10	92.90 104	82.70 16	87.13 26	95.27 47	64.99 70	95.80 134	89.34 22	91.80 71	95.93 36

tpmrst		80.57 131	79.14 146	84.84 119	90.10 143	68.28 68	81.70 299	89.72 226	77.63 75	75.96 135	79.54 286	64.94 72	92.71 240	75.43 130	77.28 189	93.55 124

ZD-MVS					96.63 8	65.50 146		93.50 77	70.74 211	85.26 47	95.19 53	64.92 73	97.29 73	87.51 40	93.01 54

miper_ehance_all_eth		77.60 186	76.44 184	81.09 215	85.70 229	64.41 176	90.65 198	88.64 263	72.31 162	67.37 237	82.52 241	64.77 74	92.64 246	70.67 169	65.30 258	86.24 247

Test_1112_low_res		79.56 151	78.60 150	82.43 172	88.24 185	60.39 254	92.09 136	87.99 277	72.10 171	71.84 179	87.42 192	64.62 75	93.04 225	65.80 215	77.30 188	93.85 119

DeepC-MVS_fast	79.48 2	87.95 18	88.00 18	87.79 24	95.86 24	68.32 66	95.74 20	94.11 55	83.82 11	83.49 63	96.19 26	64.53 76	98.44 26	83.42 73	94.88 22	96.61 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		86.62 40	86.66 39	86.50 63	96.95 5	65.70 139	94.41 48	93.45 79	67.74 240	86.19 34	96.39 20	64.38 77	97.91 42	87.33 44	93.14 53	95.90 38

MG-MVS		87.11 30	86.27 40	89.62 8	97.79 1	76.27 4	94.96 42	94.49 38	78.74 62	83.87 62	92.94 110	64.34 78	96.94 98	75.19 132	94.09 36	95.66 42

casdiffmvs		85.37 55	84.87 60	86.84 47	88.25 184	69.07 49	93.04 102	91.76 146	81.27 30	80.84 85	92.07 131	64.23 79	96.06 127	84.98 61	87.43 110	95.39 48

cl-mvsnet2		77.94 182	76.78 179	81.42 201	87.57 196	64.93 164	90.67 197	88.86 256	72.45 158	67.63 232	82.68 240	64.07 80	92.91 233	71.79 159	65.30 258	86.44 242

tpm		78.58 171	77.03 175	83.22 158	85.94 226	64.56 167	83.21 291	91.14 173	78.31 65	73.67 156	79.68 284	64.01 81	92.09 263	66.07 212	71.26 225	93.03 140

CDS-MVSNet		81.43 118	80.74 116	83.52 153	86.26 219	64.45 172	92.09 136	90.65 189	75.83 95	73.95 155	89.81 164	63.97 82	92.91 233	71.27 164	82.82 145	93.20 135
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_Test		84.16 76	83.20 81	87.05 43	91.56 114	69.82 36	89.99 219	92.05 134	77.77 72	82.84 66	86.57 200	63.93 83	96.09 123	74.91 138	89.18 96	95.25 63

APD-MVS		85.93 49	85.99 45	85.76 89	95.98 23	65.21 151	93.59 84	92.58 117	66.54 251	86.17 35	95.88 30	63.83 84	97.00 90	86.39 52	92.94 55	95.06 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs_anonymous		81.36 119	79.99 127	85.46 99	90.39 139	68.40 64	86.88 269	90.61 190	74.41 113	70.31 195	84.67 220	63.79 85	92.32 258	73.13 144	85.70 124	95.67 41

PVSNet_Blended_VisFu		83.97 79	83.50 72	85.39 103	90.02 144	66.59 118	93.77 78	91.73 147	77.43 79	77.08 129	89.81 164	63.77 86	96.97 95	79.67 101	88.21 103	92.60 150

baseline		85.01 60	84.44 64	86.71 53	88.33 181	68.73 57	90.24 210	91.82 145	81.05 34	81.18 79	92.50 120	63.69 87	96.08 125	84.45 65	86.71 118	95.32 53

CDPH-MVS		85.71 52	85.46 53	86.46 64	94.75 32	67.19 98	93.89 71	92.83 106	70.90 206	83.09 65	95.28 45	63.62 88	97.36 68	80.63 96	94.18 35	94.84 77

HyFIR lowres test		81.03 126	79.56 135	85.43 101	87.81 194	68.11 73	90.18 211	90.01 215	70.65 212	72.95 161	86.06 206	63.61 89	94.50 185	75.01 136	79.75 164	93.67 121

canonicalmvs		86.85 36	86.25 42	88.66 15	91.80 108	71.92 13	93.54 86	91.71 149	80.26 40	87.55 24	95.25 49	63.59 90	96.93 100	88.18 34	84.34 136	97.11 6

cl_fuxian		76.83 198	75.47 196	80.93 219	85.02 239	64.18 185	90.39 205	88.11 274	71.66 186	66.65 244	81.64 253	63.58 91	92.56 247	69.31 181	62.86 280	86.04 253

SteuartSystems-ACMMP		86.82 38	86.90 35	86.58 59	90.42 137	66.38 121	96.09 15	93.87 58	77.73 73	84.01 61	95.66 35	63.39 92	97.94 39	87.40 43	93.55 48	95.42 47
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		83.77 84	83.67 70	84.06 140	92.79 81	63.56 200	91.76 154	94.81 25	79.65 46	77.87 116	94.09 88	63.35 93	97.90 43	79.35 103	79.36 166	90.74 184

UniMVSNet (Re)		77.58 187	76.78 179	79.98 234	84.11 254	60.80 244	91.76 154	93.17 93	76.56 89	69.93 202	84.78 219	63.32 94	92.36 256	64.89 224	62.51 284	86.78 235

PVSNet_BlendedMVS		83.38 89	83.43 75	83.22 158	93.76 51	67.53 88	94.06 60	93.61 71	79.13 54	81.00 83	85.14 214	63.19 95	97.29 73	87.08 47	73.91 205	84.83 274

PVSNet_Blended		86.73 39	86.86 36	86.31 72	93.76 51	67.53 88	96.33 14	93.61 71	82.34 18	81.00 83	93.08 105	63.19 95	97.29 73	87.08 47	91.38 78	94.13 104

PAPM_NR		82.97 95	81.84 102	86.37 69	94.10 46	66.76 113	87.66 259	92.84 105	69.96 218	74.07 153	93.57 98	63.10 97	97.50 61	70.66 170	90.58 88	94.85 76

nrg030		80.93 127	79.86 129	84.13 139	83.69 259	68.83 55	93.23 96	91.20 168	75.55 97	75.06 144	88.22 182	63.04 98	94.74 173	81.88 84	66.88 251	88.82 204

CS-MVS		86.61 41	86.85 37	85.88 82	91.52 117	66.25 126	95.42 27	92.25 125	80.36 39	84.10 59	94.82 66	62.88 99	96.08 125	88.25 33	92.07 69	95.30 55

EI-MVSNet-UG-set		83.14 92	82.96 85	83.67 150	92.28 90	63.19 206	91.38 171	94.68 31	79.22 51	76.60 131	93.75 94	62.64 100	97.76 49	78.07 117	78.01 177	90.05 192

DeepC-MVS	77.85 3	85.52 53	85.24 55	86.37 69	88.80 171	66.64 115	92.15 132	93.68 68	81.07 33	76.91 130	93.64 96	62.59 101	98.44 26	85.50 57	92.84 57	94.03 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EIA-MVS		84.84 63	84.88 59	84.69 124	91.30 122	62.36 222	93.85 73	92.04 135	79.45 47	79.33 101	94.28 85	62.42 102	96.35 115	80.05 99	91.25 81	95.38 49

MVS_111021_HR		86.19 46	85.80 49	87.37 35	93.17 69	69.79 37	93.99 64	93.76 63	79.08 56	78.88 109	93.99 91	62.25 103	98.15 34	85.93 55	91.15 82	94.15 103

PHI-MVS		86.83 37	86.85 37	86.78 51	93.47 61	65.55 144	95.39 29	95.10 17	71.77 184	85.69 42	96.52 15	62.07 104	98.77 17	86.06 54	95.60 10	96.03 33

MP-MVS		85.02 59	84.97 58	85.17 110	92.60 84	64.27 183	93.24 95	92.27 124	73.13 142	79.63 98	94.43 75	61.90 105	97.17 80	85.00 60	92.56 60	94.06 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
jason		86.40 42	86.17 43	87.11 41	86.16 221	70.54 25	95.71 23	92.19 132	82.00 23	84.58 51	94.34 82	61.86 106	95.53 154	87.76 37	90.89 84	95.27 60
jason: jason.
PAPR		85.15 58	84.47 62	87.18 39	96.02 22	68.29 67	91.85 149	93.00 101	76.59 88	79.03 105	95.00 56	61.59 107	97.61 58	78.16 116	89.00 97	95.63 43

IS-MVSNet		80.14 139	79.41 139	82.33 176	87.91 192	60.08 259	91.97 144	88.27 271	72.90 149	71.44 185	91.73 138	61.44 108	93.66 216	62.47 240	86.53 120	93.24 133

cl-mvsnet_		76.07 205	74.67 201	80.28 227	85.15 235	61.76 230	90.12 212	88.73 259	71.16 201	65.43 249	81.57 255	61.15 109	92.95 228	66.54 205	62.17 286	86.13 251

cl-mvsnet1		76.07 205	74.67 201	80.28 227	85.14 236	61.75 231	90.12 212	88.73 259	71.16 201	65.42 250	81.60 254	61.15 109	92.94 232	66.54 205	62.16 288	86.14 249

EI-MVSNet		78.97 160	78.22 155	81.25 205	85.33 232	62.73 218	89.53 227	93.21 88	72.39 161	72.14 175	90.13 160	60.99 111	94.72 174	67.73 194	72.49 215	86.29 245

IterMVS-LS		76.49 201	75.18 199	80.43 224	84.49 247	62.74 217	90.64 199	88.80 257	72.40 160	65.16 252	81.72 251	60.98 112	92.27 259	67.74 193	64.65 269	86.29 245
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ETV-MVS		86.01 48	86.11 44	85.70 93	90.21 142	67.02 105	93.43 91	91.92 140	81.21 31	84.13 58	94.07 90	60.93 113	95.63 144	89.28 23	89.81 92	94.46 94

tpm cat1		75.30 221	72.21 237	84.58 128	88.52 174	67.77 81	78.16 324	88.02 276	61.88 288	68.45 222	76.37 309	60.65 114	94.03 204	53.77 276	74.11 202	91.93 165

TAMVS		80.37 135	79.45 138	83.13 160	85.14 236	63.37 201	91.23 178	90.76 185	74.81 111	72.65 165	88.49 173	60.63 115	92.95 228	69.41 179	81.95 151	93.08 139

ZNCC-MVS		85.33 56	85.08 57	86.06 77	93.09 72	65.65 141	93.89 71	93.41 83	73.75 130	79.94 93	94.68 70	60.61 116	98.03 37	82.63 80	93.72 44	94.52 90

thres100view900		78.37 174	77.01 176	82.46 171	91.89 104	63.21 205	91.19 182	96.33 1	72.28 164	70.45 192	87.89 186	60.31 117	95.32 158	45.16 308	77.58 182	88.83 202

thres600view7		78.00 179	76.66 181	82.03 191	91.93 101	63.69 195	91.30 176	96.33 1	72.43 159	70.46 191	87.89 186	60.31 117	94.92 169	42.64 320	76.64 193	87.48 222

CHOSEN 1792x2688		84.98 61	83.45 74	89.57 9	89.94 146	75.14 5	92.07 138	92.32 122	81.87 24	75.68 137	88.27 178	60.18 119	98.60 21	80.46 98	90.27 91	94.96 74

hse-mvs3		83.01 94	82.56 94	84.35 134	89.34 158	62.02 227	92.72 114	93.76 63	81.45 27	82.73 67	92.25 129	60.11 120	97.13 83	87.69 38	62.96 278	93.91 115

hse-mvs2		81.12 124	81.11 113	81.16 208	86.52 214	57.48 289	89.40 230	91.16 170	81.45 27	82.73 67	90.49 152	60.11 120	94.58 177	87.69 38	60.41 305	91.41 173

tfpn200view9		78.79 165	77.43 169	82.88 162	92.21 93	64.49 169	92.05 139	96.28 4	73.48 137	71.75 181	88.26 179	60.07 122	95.32 158	45.16 308	77.58 182	88.83 202

thres400		78.68 168	77.43 169	82.43 172	92.21 93	64.49 169	92.05 139	96.28 4	73.48 137	71.75 181	88.26 179	60.07 122	95.32 158	45.16 308	77.58 182	87.48 222

test_part1		79.63 149	77.86 163	84.93 114	92.50 86	71.43 15	94.15 55	91.08 176	72.51 156	70.66 188	84.98 216	59.84 124	95.07 163	72.07 158	62.94 279	88.30 215

diffmvs		84.28 71	83.83 69	85.61 95	87.40 201	68.02 75	90.88 191	89.24 238	80.54 37	81.64 75	92.52 119	59.83 125	94.52 184	87.32 45	85.11 128	94.29 95

MVS		84.66 66	82.86 88	90.06 2	90.93 128	74.56 6	87.91 255	95.54 9	68.55 236	72.35 174	94.71 69	59.78 126	98.90 14	81.29 93	94.69 28	96.74 10

thres200		79.66 148	78.33 152	83.66 151	92.54 85	65.82 138	93.06 100	96.31 3	74.90 110	73.30 158	88.66 171	59.67 127	95.61 146	47.84 298	78.67 173	89.56 199

Effi-MVS+		83.82 83	82.76 90	86.99 45	89.56 154	69.40 42	91.35 173	86.12 294	72.59 153	83.22 64	92.81 117	59.60 128	96.01 131	81.76 85	87.80 107	95.56 45

eth_miper_zixun_eth		75.96 211	74.40 209	80.66 221	84.66 243	63.02 208	89.28 232	88.27 271	71.88 177	65.73 247	81.65 252	59.45 129	92.81 236	68.13 189	60.53 302	86.14 249

ACMMP_NAP		86.05 47	85.80 49	86.80 50	91.58 113	67.53 88	91.79 151	93.49 78	74.93 109	84.61 50	95.30 44	59.42 130	97.92 41	86.13 53	94.92 17	94.94 75

GST-MVS		84.63 67	84.29 66	85.66 94	92.82 78	65.27 149	93.04 102	93.13 95	73.20 140	78.89 106	94.18 87	59.41 131	97.85 46	81.45 89	92.48 63	93.86 118

UA-Net		80.02 142	79.65 132	81.11 211	89.33 159	57.72 284	86.33 272	89.00 252	77.44 78	81.01 82	89.15 168	59.33 132	95.90 132	61.01 247	84.28 139	89.73 196

NR-MVSNet		76.05 208	74.59 204	80.44 223	82.96 268	62.18 226	90.83 193	91.73 147	77.12 81	60.96 282	86.35 201	59.28 133	91.80 268	60.74 248	61.34 297	87.35 226

MP-MVS-pluss		85.24 57	85.13 56	85.56 96	91.42 119	65.59 143	91.54 163	92.51 119	74.56 112	80.62 86	95.64 36	59.15 134	97.00 90	86.94 49	93.80 41	94.07 108
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		84.73 64	84.40 65	85.72 90	93.75 53	65.01 160	93.50 88	93.19 91	72.19 167	79.22 102	94.93 60	59.04 135	97.67 51	81.55 87	92.21 64	94.49 92

#test#		84.98 61	84.74 61	85.72 90	93.75 53	65.01 160	94.09 58	93.19 91	73.55 136	79.22 102	94.93 60	59.04 135	97.67 51	82.66 78	92.21 64	94.49 92

MSLP-MVS++		86.27 44	85.91 47	87.35 36	92.01 98	68.97 53	95.04 40	92.70 109	79.04 57	81.50 76	96.50 17	58.98 137	96.78 104	83.49 72	93.93 39	96.29 26

Patchmatch-test		65.86 291	60.94 303	80.62 222	83.75 258	58.83 273	58.91 351	75.26 339	44.50 346	50.95 325	77.09 303	58.81 138	87.90 309	35.13 340	64.03 273	95.12 67

EPNet_dtu		78.80 164	79.26 143	77.43 271	88.06 188	49.71 330	91.96 145	91.95 139	77.67 74	76.56 132	91.28 141	58.51 139	90.20 291	56.37 266	80.95 159	92.39 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VNet		86.20 45	85.65 52	87.84 23	93.92 47	69.99 31	95.73 22	95.94 6	78.43 64	86.00 37	93.07 107	58.22 140	97.00 90	85.22 59	84.33 137	96.52 17

TESTMET0.1,1		82.41 103	81.98 101	83.72 148	88.08 187	63.74 192	92.70 116	93.77 62	79.30 49	77.61 121	87.57 190	58.19 141	94.08 198	73.91 142	86.68 119	93.33 130

原ACMM1				84.42 132	93.21 67	64.27 183		93.40 84	65.39 259	79.51 99	92.50 120	58.11 142	96.69 108	65.27 222	93.96 38	92.32 157

sam_mvs1												57.85 143				94.68 81

CR-MVSNet		73.79 236	70.82 248	82.70 166	83.15 266	67.96 76	70.25 334	84.00 313	73.67 134	69.97 200	72.41 324	57.82 144	89.48 297	52.99 279	73.13 209	90.64 186

Patchmtry		67.53 283	63.93 288	78.34 259	82.12 273	64.38 177	68.72 338	84.00 313	48.23 339	59.24 289	72.41 324	57.82 144	89.27 298	46.10 305	56.68 317	81.36 311

patchmatchnet-post											67.62 337	57.62 146	90.25 286

PCF-MVS	73.15 9	79.29 155	77.63 166	84.29 136	86.06 222	65.96 133	87.03 265	91.10 174	69.86 220	69.79 204	90.64 147	57.54 147	96.59 109	64.37 227	82.29 147	90.32 188
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.14 122	80.01 126	84.51 130	90.24 141	65.86 135	94.12 57	89.15 244	73.81 129	75.37 143	88.26 179	57.26 148	94.53 183	66.97 201	84.92 129	93.15 136

miper_lstm_enhance		73.05 240	71.73 242	77.03 276	83.80 257	58.32 279	81.76 297	88.88 254	69.80 221	61.01 281	78.23 293	57.19 149	87.51 315	65.34 221	59.53 307	85.27 271

1121		81.25 120	80.05 125	84.87 118	92.30 89	64.31 180	87.91 255	91.39 162	59.44 303	79.94 93	92.91 111	57.09 150	97.01 88	66.63 202	92.81 58	93.29 131

PatchT		69.11 268	65.37 279	80.32 225	82.07 274	63.68 196	67.96 343	87.62 281	50.86 331	69.37 205	65.18 340	57.09 150	88.53 304	41.59 323	66.60 253	88.74 205

testdata				81.34 203	89.02 166	57.72 284		89.84 219	58.65 307	85.32 46	94.09 88	57.03 152	93.28 222	69.34 180	90.56 89	93.03 140

PatchmatchNet		77.46 188	74.63 203	85.96 80	89.55 155	70.35 27	79.97 315	89.55 229	72.23 166	70.94 186	76.91 305	57.03 152	92.79 238	54.27 273	81.17 157	94.74 79
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_yl		84.28 71	83.16 82	87.64 26	94.52 36	69.24 46	95.78 17	95.09 18	69.19 228	81.09 80	92.88 114	57.00 154	97.44 63	81.11 94	81.76 152	96.23 28

DCV-MVSNet		84.28 71	83.16 82	87.64 26	94.52 36	69.24 46	95.78 17	95.09 18	69.19 228	81.09 80	92.88 114	57.00 154	97.44 63	81.11 94	81.76 152	96.23 28

region2R		84.36 69	84.03 68	85.36 104	93.54 59	64.31 180	93.43 91	92.95 102	72.16 170	78.86 110	94.84 65	56.97 156	97.53 60	81.38 91	92.11 68	94.24 97

新几何1				84.73 121	92.32 88	64.28 182		91.46 160	59.56 302	79.77 96	92.90 112	56.95 157	96.57 111	63.40 232	92.91 56	93.34 128

WR-MVS		76.76 199	75.74 194	79.82 239	84.60 244	62.27 225	92.60 121	92.51 119	76.06 92	67.87 229	85.34 212	56.76 158	90.24 289	62.20 241	63.69 277	86.94 233

HPM-MVS		83.25 90	82.95 86	84.17 138	92.25 91	62.88 215	90.91 188	91.86 142	70.30 215	77.12 127	93.96 92	56.75 159	96.28 116	82.04 83	91.34 80	93.34 128
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		82.71 100	82.38 97	83.73 147	89.25 161	59.58 265	92.24 130	94.89 21	77.96 70	79.86 95	92.38 125	56.70 160	97.05 85	77.26 122	80.86 160	94.55 86

ACMMPR		84.37 68	84.06 67	85.28 106	93.56 58	64.37 178	93.50 88	93.15 94	72.19 167	78.85 111	94.86 64	56.69 161	97.45 62	81.55 87	92.20 66	94.02 111

FMVSNet3		77.73 185	76.04 189	82.80 163	91.20 125	68.99 52	91.87 147	91.99 137	73.35 139	67.04 239	83.19 236	56.62 162	92.14 260	59.80 255	69.34 233	87.28 228

Patchmatch-RL test		68.17 277	64.49 285	79.19 251	71.22 338	53.93 310	70.07 336	71.54 346	69.22 227	56.79 305	62.89 343	56.58 163	88.61 301	69.53 178	52.61 326	95.03 73

test_post											23.01 357	56.49 164	92.67 243

RPMNet		70.42 259	65.68 275	84.63 127	83.15 266	67.96 76	70.25 334	90.45 191	46.83 342	69.97 200	65.10 341	56.48 165	95.30 161	35.79 339	73.13 209	90.64 186

DU-MVS		76.86 195	75.84 192	79.91 236	82.96 268	60.26 255	91.26 177	91.54 155	76.46 90	68.88 213	86.35 201	56.16 166	92.13 261	66.38 208	62.55 282	87.35 226

Baseline_NR-MVSNet		73.99 233	72.83 228	77.48 270	80.78 282	59.29 270	91.79 151	84.55 308	68.85 232	68.99 211	80.70 269	56.16 166	92.04 264	62.67 238	60.98 299	81.11 312

API-MVS		82.28 105	80.53 121	87.54 31	96.13 20	70.59 24	93.63 82	91.04 179	65.72 258	75.45 142	92.83 116	56.11 168	98.89 15	64.10 228	89.75 95	93.15 136

zzz-MVS		84.73 64	84.47 62	85.50 97	91.89 104	65.16 153	91.55 162	92.23 126	75.32 103	80.53 87	95.21 51	56.06 169	97.16 81	84.86 63	92.55 61	94.18 99

MTAPA		83.91 81	83.38 79	85.50 97	91.89 104	65.16 153	81.75 298	92.23 126	75.32 103	80.53 87	95.21 51	56.06 169	97.16 81	84.86 63	92.55 61	94.18 99

JIA-IIPM		66.06 290	62.45 297	76.88 280	81.42 279	54.45 309	57.49 352	88.67 261	49.36 335	63.86 264	46.86 349	56.06 169	90.25 286	49.53 288	68.83 237	85.95 256

v148		76.19 203	74.47 208	81.36 202	80.05 293	64.44 173	91.75 156	90.23 205	73.68 133	67.13 238	80.84 268	55.92 172	93.86 213	68.95 185	61.73 293	85.76 262

WR-MVS_H		70.59 257	69.94 253	72.53 308	81.03 280	51.43 321	87.35 263	92.03 136	67.38 245	60.23 285	80.70 269	55.84 173	83.45 334	46.33 304	58.58 312	82.72 297

AUN-MVS		78.37 174	77.43 169	81.17 207	86.60 213	57.45 290	89.46 229	91.16 170	74.11 120	74.40 148	90.49 152	55.52 174	94.57 178	74.73 141	60.43 304	91.48 171

XVS		83.87 82	83.47 73	85.05 111	93.22 65	63.78 190	92.92 109	92.66 112	73.99 122	78.18 114	94.31 84	55.25 175	97.41 65	79.16 105	91.58 75	93.95 113

X-MVStestdata		76.86 195	74.13 215	85.05 111	93.22 65	63.78 190	92.92 109	92.66 112	73.99 122	78.18 114	10.19 362	55.25 175	97.41 65	79.16 105	91.58 75	93.95 113

BH-w/o		80.49 134	79.30 142	84.05 141	90.83 133	64.36 179	93.60 83	89.42 233	74.35 115	69.09 208	90.15 159	55.23 177	95.61 146	64.61 225	86.43 122	92.17 163

CP-MVS		83.71 86	83.40 78	84.65 125	93.14 70	63.84 188	94.59 46	92.28 123	71.03 204	77.41 123	94.92 62	55.21 178	96.19 119	81.32 92	90.70 86	93.91 115

PGM-MVS		83.25 90	82.70 92	84.92 115	92.81 80	64.07 186	90.44 202	92.20 131	71.28 199	77.23 126	94.43 75	55.17 179	97.31 72	79.33 104	91.38 78	93.37 127

tpmvs		72.88 244	69.76 256	82.22 181	90.98 127	67.05 103	78.22 323	88.30 269	63.10 276	64.35 262	74.98 316	55.09 180	94.27 191	43.25 314	69.57 232	85.34 269

v8		75.35 220	73.26 225	81.61 198	80.67 284	66.82 110	89.54 226	89.27 237	71.65 187	63.30 270	80.30 277	54.99 181	94.06 200	67.33 198	62.33 285	83.94 280

sam_mvs												54.91 182

EPMVS		78.49 173	75.98 190	86.02 78	91.21 124	69.68 40	80.23 311	91.20 168	75.25 105	72.48 170	78.11 294	54.65 183	93.69 215	57.66 263	83.04 143	94.69 80

ab-mvs		80.18 138	78.31 153	85.80 87	88.44 178	65.49 147	83.00 293	92.67 111	71.82 182	77.36 124	85.01 215	54.50 184	96.59 109	76.35 128	75.63 198	95.32 53

KD-MVS_2432*1600		69.03 269	66.37 271	77.01 277	85.56 230	61.06 240	81.44 302	90.25 203	67.27 246	58.00 299	76.53 307	54.49 185	87.63 313	48.04 295	35.77 349	82.34 303

miper_refine_blended		69.03 269	66.37 271	77.01 277	85.56 230	61.06 240	81.44 302	90.25 203	67.27 246	58.00 299	76.53 307	54.49 185	87.63 313	48.04 295	35.77 349	82.34 303

DP-MVS Recon		82.73 98	81.65 105	85.98 79	97.31 4	67.06 102	95.15 34	91.99 137	69.08 231	76.50 133	93.89 93	54.48 187	98.20 32	70.76 168	85.66 125	92.69 147

XXY-MVS		77.94 182	76.44 184	82.43 172	82.60 270	64.44 173	92.01 141	91.83 144	73.59 135	70.00 199	85.82 208	54.43 188	94.76 171	69.63 176	68.02 244	88.10 217

MDTV_nov1_ep13_2view						59.90 261	80.13 313		67.65 243	72.79 163		54.33 189		59.83 254		92.58 151

Test By Simon												54.21 190

MAR-MVS		84.18 75	83.43 75	86.44 65	96.25 19	65.93 134	94.28 51	94.27 50	74.41 113	79.16 104	95.61 37	53.99 191	98.88 16	69.62 177	93.26 51	94.50 91
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
test-LLR		80.10 140	79.56 135	81.72 195	86.93 210	61.17 237	92.70 116	91.54 155	71.51 196	75.62 138	86.94 197	53.83 192	92.38 254	72.21 155	84.76 132	91.60 168

test0.0.03 1		72.76 245	72.71 231	72.88 306	80.25 291	47.99 336	91.22 179	89.45 231	71.51 196	62.51 277	87.66 188	53.83 192	85.06 325	50.16 285	67.84 247	85.58 263

v2v482		77.42 189	75.65 195	82.73 165	80.38 287	67.13 101	91.85 149	90.23 205	75.09 107	69.37 205	83.39 234	53.79 194	94.44 186	71.77 160	65.00 264	86.63 240

SR-MVS		82.81 97	82.58 93	83.50 155	93.35 62	61.16 239	92.23 131	91.28 167	64.48 264	81.27 78	95.28 45	53.71 195	95.86 133	82.87 76	88.77 99	93.49 126

pcd_1.5k_mvsjas		4.46 336	5.95 339	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	53.55 196	0.00 366	0.00 364	0.00 363	0.00 362

PS-MVSNAJss		77.26 192	76.31 186	80.13 231	80.64 285	59.16 271	90.63 201	91.06 177	72.80 150	68.58 220	84.57 222	53.55 196	93.96 207	72.97 145	71.96 219	87.27 229

PS-MVSNAJ		88.14 14	87.61 24	89.71 7	92.06 95	76.72 1	95.75 19	93.26 87	83.86 10	89.55 16	96.06 28	53.55 196	97.89 44	91.10 13	93.31 50	94.54 88

mPP-MVS		82.96 96	82.44 96	84.52 129	92.83 76	62.92 213	92.76 112	91.85 143	71.52 195	75.61 140	94.24 86	53.48 199	96.99 93	78.97 108	90.73 85	93.64 123

xiu_mvs_v2_base		87.92 19	87.38 29	89.55 10	91.41 121	76.43 3	95.74 20	93.12 96	83.53 12	89.55 16	95.95 29	53.45 200	97.68 50	91.07 14	92.62 59	94.54 88

test_post1							78.95 317				20.70 360	53.05 201	91.50 278	60.43 250

MDTV_nov1_ep13			72.61 232		89.06 165	68.48 62	80.33 309	90.11 209	71.84 181	71.81 180	75.92 313	53.01 202	93.92 209	48.04 295	73.38 207

test222					89.77 149	61.60 233	89.55 225	89.42 233	56.83 316	77.28 125	92.43 124	52.76 203			91.14 83	93.09 138

v1144		76.73 200	74.88 200	82.27 178	80.23 292	66.60 117	91.68 158	90.21 207	73.69 132	69.06 210	81.89 248	52.73 204	94.40 187	69.21 182	65.23 261	85.80 259

v10		74.77 227	72.54 234	81.46 200	80.33 290	66.71 114	89.15 236	89.08 248	70.94 205	63.08 271	79.86 282	52.52 205	94.04 203	65.70 216	62.17 286	83.64 282

CLD-MVS		82.73 98	82.35 98	83.86 143	87.90 193	67.65 85	95.45 26	92.18 133	85.06 7	72.58 167	92.27 128	52.46 206	95.78 135	84.18 66	79.06 169	88.16 216
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TranMVSNet+NR-MVSNet		75.86 213	74.52 207	79.89 237	82.44 271	60.64 251	91.37 172	91.37 163	76.63 87	67.65 231	86.21 204	52.37 207	91.55 273	61.84 243	60.81 300	87.48 222

VPA-MVSNet		79.03 158	78.00 158	82.11 189	85.95 224	64.48 171	93.22 97	94.66 32	75.05 108	74.04 154	84.95 217	52.17 208	93.52 218	74.90 139	67.04 250	88.32 214

APD-MVS_3200maxsize		81.64 116	81.32 108	82.59 170	92.36 87	58.74 274	91.39 169	91.01 180	63.35 272	79.72 97	94.62 71	51.82 209	96.14 121	79.71 100	87.93 106	92.89 145

dp		75.01 225	72.09 238	83.76 144	89.28 160	66.22 128	79.96 316	89.75 221	71.16 201	67.80 230	77.19 302	51.81 210	92.54 248	50.39 284	71.44 224	92.51 153

v144192		76.05 208	74.03 216	82.12 186	79.50 298	66.55 119	91.39 169	89.71 227	72.30 163	68.17 223	81.33 260	51.75 211	94.03 204	67.94 191	64.19 271	85.77 260

BH-untuned		78.68 168	77.08 174	83.48 156	89.84 148	63.74 192	92.70 116	88.59 264	71.57 193	66.83 242	88.65 172	51.75 211	95.39 156	59.03 258	84.77 131	91.32 177

HQP2-MVS												51.63 213

HQP-MVS		81.14 122	80.64 119	82.64 168	87.54 197	63.66 197	94.06 60	91.70 150	79.80 42	74.18 149	90.30 156	51.63 213	95.61 146	77.63 120	78.90 170	88.63 206

V42		76.46 202	74.55 206	82.19 182	79.14 304	67.82 80	90.26 209	89.42 233	73.75 130	68.63 219	81.89 248	51.31 215	94.09 197	71.69 162	64.84 265	84.66 275

test1172		81.90 113	81.83 103	82.13 185	93.23 64	57.52 288	91.61 161	90.98 181	64.32 266	80.20 91	95.00 56	51.26 216	95.61 146	81.73 86	88.13 104	93.26 132

SR-MVS-dyc-post		81.06 125	80.70 117	82.15 183	92.02 96	58.56 276	90.90 189	90.45 191	62.76 278	78.89 106	94.46 73	51.26 216	95.61 146	78.77 111	86.77 116	92.28 159

CL-MVSNet_2432*1600		69.92 262	68.09 264	75.41 288	73.25 333	55.90 301	90.05 215	89.90 217	69.96 218	61.96 280	76.54 306	51.05 218	87.64 312	49.51 289	50.59 331	82.70 299

TransMVSNet (Re)		70.07 261	67.66 265	77.31 274	80.62 286	59.13 272	91.78 153	84.94 304	65.97 255	60.08 286	80.44 274	50.78 219	91.87 266	48.84 291	45.46 338	80.94 314

HQP_MVS		80.34 136	79.75 131	82.12 186	86.94 208	62.42 220	93.13 98	91.31 164	78.81 60	72.53 168	89.14 169	50.66 220	95.55 152	76.74 123	78.53 175	88.39 212

plane_prior6					87.23 203	62.32 223						50.66 220

ACMMP		81.49 117	80.67 118	83.93 142	91.71 110	62.90 214	92.13 133	92.22 130	71.79 183	71.68 183	93.49 100	50.32 222	96.96 96	78.47 113	84.22 141	91.93 165
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
MVS_111021_LR		82.02 111	81.52 106	83.51 154	88.42 179	62.88 215	89.77 222	88.93 253	76.78 86	75.55 141	93.10 103	50.31 223	95.38 157	83.82 71	87.02 112	92.26 162

1314		80.70 129	78.95 147	85.94 81	87.77 195	67.56 86	87.91 255	92.55 118	72.17 169	67.44 233	93.09 104	50.27 224	97.04 87	71.68 163	87.64 108	93.23 134

CP-MVSNet		70.50 258	69.91 254	72.26 311	80.71 283	51.00 324	87.23 264	90.30 201	67.84 239	59.64 287	82.69 239	50.23 225	82.30 342	51.28 281	59.28 308	83.46 287

LCM-MVSNet-Re		72.93 242	71.84 240	76.18 285	88.49 175	48.02 335	80.07 314	70.17 347	73.96 125	52.25 318	80.09 281	49.98 226	88.24 306	67.35 196	84.23 140	92.28 159

Vis-MVSNet		80.92 128	79.98 128	83.74 145	88.48 176	61.80 229	93.44 90	88.26 273	73.96 125	77.73 117	91.76 136	49.94 227	94.76 171	65.84 214	90.37 90	94.65 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		75.98 210	73.92 218	82.15 183	79.73 294	66.24 127	91.22 179	89.75 221	72.67 152	68.49 221	81.42 258	49.86 228	94.27 191	67.08 199	65.02 263	85.95 256

test-mter		79.96 143	79.38 141	81.72 195	86.93 210	61.17 237	92.70 116	91.54 155	73.85 127	75.62 138	86.94 197	49.84 229	92.38 254	72.21 155	84.76 132	91.60 168

cdsmvs_eth3d_5k		19.86 331	26.47 330	0.00 348	0.00 369	0.00 370	0.00 360	93.45 79	0.00 365	0.00 366	95.27 47	49.56 230	0.00 366	0.00 364	0.00 363	0.00 362

3Dnovator+	73.60 7	82.10 110	80.60 120	86.60 57	90.89 131	66.80 112	95.20 32	93.44 81	74.05 121	67.42 234	92.49 122	49.46 231	97.65 55	70.80 167	91.68 73	95.33 51

MVP-Stereo		77.12 194	76.23 187	79.79 240	81.72 276	66.34 123	89.29 231	90.88 183	70.56 213	62.01 279	82.88 237	49.34 232	94.13 195	65.55 219	93.80 41	78.88 331
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
RE-MVS-def			80.48 122		92.02 96	58.56 276	90.90 189	90.45 191	62.76 278	78.89 106	94.46 73	49.30 233		78.77 111	86.77 116	92.28 159

OMC-MVS		78.67 170	77.91 162	80.95 218	85.76 228	57.40 291	88.49 247	88.67 261	73.85 127	72.43 172	92.10 130	49.29 234	94.55 181	72.73 149	77.89 178	90.91 183

VPNet		78.82 163	77.53 168	82.70 166	84.52 246	66.44 120	93.93 68	92.23 126	80.46 38	72.60 166	88.38 176	49.18 235	93.13 224	72.47 153	63.97 275	88.55 208

CVMVSNet		74.04 232	74.27 211	73.33 302	85.33 232	43.94 345	89.53 227	88.39 267	54.33 323	70.37 193	90.13 160	49.17 236	84.05 328	61.83 244	79.36 166	91.99 164

v1921920		75.63 218	73.49 223	82.06 190	79.38 299	66.35 122	91.07 187	89.48 230	71.98 172	67.99 224	81.22 263	49.16 237	93.90 210	66.56 204	64.56 270	85.92 258

pm-mvs1		72.89 243	71.09 246	78.26 262	79.10 305	57.62 286	90.80 194	89.30 236	67.66 242	62.91 273	81.78 250	49.11 238	92.95 228	60.29 252	58.89 310	84.22 278

pmmvs4		73.92 234	71.81 241	80.25 229	79.17 302	65.24 150	87.43 262	87.26 286	67.64 244	63.46 268	83.91 228	48.96 239	91.53 277	62.94 235	65.49 257	83.96 279

TAPA-MVS	70.22 12	74.94 226	73.53 222	79.17 252	90.40 138	52.07 318	89.19 235	89.61 228	62.69 280	70.07 197	92.67 118	48.89 240	94.32 188	38.26 334	79.97 162	91.12 181
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator	73.91 6	82.69 101	80.82 115	88.31 19	89.57 153	71.26 17	92.60 121	94.39 46	78.84 59	67.89 228	92.48 123	48.42 241	98.52 22	68.80 187	94.40 33	95.15 65

CPTT-MVS		79.59 150	79.16 145	80.89 220	91.54 116	59.80 262	92.10 135	88.54 266	60.42 295	72.96 160	93.28 102	48.27 242	92.80 237	78.89 110	86.50 121	90.06 191

GBi-Net		75.65 216	73.83 219	81.10 212	88.85 168	65.11 156	90.01 216	90.32 197	70.84 207	67.04 239	80.25 278	48.03 243	91.54 274	59.80 255	69.34 233	86.64 237

test1		75.65 216	73.83 219	81.10 212	88.85 168	65.11 156	90.01 216	90.32 197	70.84 207	67.04 239	80.25 278	48.03 243	91.54 274	59.80 255	69.34 233	86.64 237

FMVSNet2		76.07 205	74.01 217	82.26 180	88.85 168	67.66 84	91.33 174	91.61 153	70.84 207	65.98 246	82.25 244	48.03 243	92.00 265	58.46 260	68.73 239	87.10 230

LFMVS		84.34 70	82.73 91	89.18 11	94.76 31	73.25 10	94.99 41	91.89 141	71.90 175	82.16 71	93.49 100	47.98 246	97.05 85	82.55 81	84.82 130	97.25 5

QAPM		79.95 144	77.39 172	87.64 26	89.63 152	71.41 16	93.30 94	93.70 67	65.34 261	67.39 236	91.75 137	47.83 247	98.96 12	57.71 262	89.81 92	92.54 152

HPM-MVS_fast		80.25 137	79.55 137	82.33 176	91.55 115	59.95 260	91.32 175	89.16 243	65.23 262	74.71 146	93.07 107	47.81 248	95.74 137	74.87 140	88.23 102	91.31 178

CANet_DTU		84.09 77	83.52 71	85.81 86	90.30 140	66.82 110	91.87 147	89.01 251	85.27 6	86.09 36	93.74 95	47.71 249	96.98 94	77.90 119	89.78 94	93.65 122

abl_6		79.82 146	79.20 144	81.70 197	89.85 147	58.34 278	88.47 248	90.07 210	62.56 281	77.71 118	93.08 105	47.65 250	96.78 104	77.94 118	85.45 127	89.99 193

v1240		75.21 223	72.98 227	81.88 192	79.20 301	66.00 131	90.75 196	89.11 247	71.63 191	67.41 235	81.22 263	47.36 251	93.87 211	65.46 220	64.72 268	85.77 260

PEN-MVS		69.46 266	68.56 260	72.17 313	79.27 300	49.71 330	86.90 268	89.24 238	67.24 249	59.08 292	82.51 242	47.23 252	83.54 333	48.42 293	57.12 313	83.25 290

CNLPA		74.31 230	72.30 236	80.32 225	91.49 118	61.66 232	90.85 192	80.72 327	56.67 317	63.85 265	90.64 147	46.75 253	90.84 281	53.79 275	75.99 197	88.47 211

114514_t		79.17 157	77.67 164	83.68 149	95.32 26	65.53 145	92.85 111	91.60 154	63.49 271	67.92 226	90.63 149	46.65 254	95.72 142	67.01 200	83.54 142	89.79 194

PS-CasMVS		69.86 264	69.13 258	72.07 314	80.35 289	50.57 326	87.02 266	89.75 221	67.27 246	59.19 291	82.28 243	46.58 255	82.24 343	50.69 283	59.02 309	83.39 289

DTE-MVSNet		68.46 275	67.33 267	71.87 316	77.94 317	49.00 333	86.16 273	88.58 265	66.36 253	58.19 296	82.21 245	46.36 256	83.87 331	44.97 311	55.17 320	82.73 296

PMMVS		81.98 112	82.04 100	81.78 193	89.76 150	56.17 298	91.13 184	90.69 186	77.96 70	80.09 92	93.57 98	46.33 257	94.99 165	81.41 90	87.46 109	94.17 101

OPM-MVS		79.00 159	78.09 156	81.73 194	83.52 262	63.83 189	91.64 160	90.30 201	76.36 91	71.97 178	89.93 163	46.30 258	95.17 162	75.10 133	77.70 180	86.19 248

BH-RMVSNet		79.46 154	77.65 165	84.89 116	91.68 111	65.66 140	93.55 85	88.09 275	72.93 147	73.37 157	91.12 142	46.20 259	96.12 122	56.28 267	85.61 126	92.91 144

TR-MVS		78.77 166	77.37 173	82.95 161	90.49 136	60.88 243	93.67 80	90.07 210	70.08 217	74.51 147	91.37 140	45.69 260	95.70 143	60.12 253	80.32 161	92.29 158

IterMVS-SCA-FT		71.55 253	69.97 252	76.32 283	81.48 277	60.67 250	87.64 260	85.99 295	66.17 254	59.50 288	78.88 288	45.53 261	83.65 332	62.58 239	61.93 289	84.63 277

SCA		75.82 214	72.76 229	85.01 113	86.63 212	70.08 29	81.06 305	89.19 241	71.60 192	70.01 198	77.09 303	45.53 261	90.25 286	60.43 250	73.27 208	94.68 81

IterMVS		72.65 249	70.83 247	78.09 264	82.17 272	62.96 210	87.64 260	86.28 291	71.56 194	60.44 284	78.85 289	45.42 263	86.66 319	63.30 233	61.83 290	84.65 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		76.14 204	75.28 198	78.72 257	83.22 264	55.17 305	89.87 220	87.78 279	75.42 99	67.98 225	81.43 257	45.08 264	92.52 249	75.08 134	71.63 220	88.48 209

mvs-test1		78.74 167	77.95 160	81.14 209	83.22 264	57.13 293	93.96 65	87.78 279	75.42 99	72.68 164	90.80 146	45.08 264	94.54 182	75.08 134	77.49 185	91.74 167

XVG-OURS-SEG-HR		74.70 228	73.08 226	79.57 246	78.25 314	57.33 292	80.49 307	87.32 284	63.22 274	68.76 217	90.12 162	44.89 266	91.59 272	70.55 171	74.09 203	89.79 194

v7n		71.31 254	68.65 259	79.28 250	76.40 325	60.77 246	86.71 270	89.45 231	64.17 267	58.77 295	78.24 292	44.59 267	93.54 217	57.76 261	61.75 292	83.52 285

pmmvs5		73.35 238	71.52 243	78.86 256	78.64 311	60.61 252	91.08 185	86.90 287	67.69 241	63.32 269	83.64 229	44.33 268	90.53 283	62.04 242	66.02 256	85.46 266

RRT_MVS		77.38 190	76.59 182	79.77 241	90.91 130	63.61 199	91.15 183	90.91 182	72.28 164	72.06 177	87.28 195	43.92 269	89.04 300	73.32 143	67.47 248	86.67 236

OpenMVS	70.45 11	78.54 172	75.92 191	86.41 68	85.93 227	71.68 14	92.74 113	92.51 119	66.49 252	64.56 257	91.96 132	43.88 270	98.10 35	54.61 271	90.65 87	89.44 200

AdaColmap		78.94 161	77.00 177	84.76 120	96.34 16	65.86 135	92.66 120	87.97 278	62.18 284	70.56 189	92.37 126	43.53 271	97.35 69	64.50 226	82.86 144	91.05 182

tfpnnormal		70.10 260	67.36 266	78.32 260	83.45 263	60.97 242	88.85 241	92.77 107	64.85 263	60.83 283	78.53 290	43.52 272	93.48 219	31.73 349	61.70 294	80.52 319

test_djsdf		73.76 237	72.56 233	77.39 272	77.00 322	53.93 310	89.07 238	90.69 186	65.80 256	63.92 263	82.03 247	43.14 273	92.67 243	72.83 147	68.53 240	85.57 264

GA-MVS		78.33 176	76.23 187	84.65 125	83.65 260	66.30 124	91.44 164	90.14 208	76.01 93	70.32 194	84.02 226	42.50 274	94.72 174	70.98 165	77.00 191	92.94 143

PLC	68.80 14	75.23 222	73.68 221	79.86 238	92.93 74	58.68 275	90.64 199	88.30 269	60.90 292	64.43 261	90.53 150	42.38 275	94.57 178	56.52 265	76.54 194	86.33 243
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		73.80 235	72.02 239	79.15 254	79.15 303	62.97 209	88.58 246	90.07 210	72.94 146	59.22 290	78.30 291	42.31 276	92.70 242	65.59 218	72.00 218	81.79 309

bset_n11_16_dypcd		75.95 212	74.16 213	81.30 204	76.91 323	65.14 155	88.89 240	87.48 282	74.30 117	69.90 203	83.40 233	42.16 277	92.42 252	78.39 114	66.03 255	86.32 244

Fast-Effi-MVS+-dtu		75.04 224	73.37 224	80.07 232	80.86 281	59.52 266	91.20 181	85.38 299	71.90 175	65.20 251	84.84 218	41.46 278	92.97 227	66.50 207	72.96 211	87.73 219

MS-PatchMatch		77.90 184	76.50 183	82.12 186	85.99 223	69.95 34	91.75 156	92.70 109	73.97 124	62.58 276	84.44 223	41.11 279	95.78 135	63.76 231	92.17 67	80.62 318

our_test_3		68.29 276	64.69 282	79.11 255	78.92 306	64.85 165	88.40 250	85.06 302	60.32 297	52.68 316	76.12 311	40.81 280	89.80 296	44.25 313	55.65 318	82.67 301

XVG-OURS		74.25 231	72.46 235	79.63 244	78.45 313	57.59 287	80.33 309	87.39 283	63.86 269	68.76 217	89.62 166	40.50 281	91.72 270	69.00 184	74.25 201	89.58 197

VDD-MVS		83.06 93	81.81 104	86.81 49	90.86 132	67.70 83	95.40 28	91.50 158	75.46 98	81.78 73	92.34 127	40.09 282	97.13 83	86.85 50	82.04 150	95.60 44

DP-MVS		69.90 263	66.48 269	80.14 230	95.36 25	62.93 211	89.56 224	76.11 334	50.27 333	57.69 302	85.23 213	39.68 283	95.73 138	33.35 343	71.05 226	81.78 310

ppachtmachnet_test		67.72 280	63.70 289	79.77 241	78.92 306	66.04 130	88.68 244	82.90 322	60.11 299	55.45 307	75.96 312	39.19 284	90.55 282	39.53 329	52.55 327	82.71 298

ADS-MVSNet2		66.90 286	63.44 291	77.26 275	88.06 188	60.70 249	68.01 341	75.56 337	57.57 309	64.48 258	69.87 333	38.68 285	84.10 327	40.87 325	67.89 245	86.97 231

ADS-MVSNet		68.54 274	64.38 287	81.03 216	88.06 188	66.90 108	68.01 341	84.02 312	57.57 309	64.48 258	69.87 333	38.68 285	89.21 299	40.87 325	67.89 245	86.97 231

LPG-MVS_test		75.82 214	74.58 205	79.56 247	84.31 251	59.37 268	90.44 202	89.73 224	69.49 223	64.86 253	88.42 174	38.65 287	94.30 189	72.56 151	72.76 212	85.01 272

LGP-MVS_train				79.56 247	84.31 251	59.37 268		89.73 224	69.49 223	64.86 253	88.42 174	38.65 287	94.30 189	72.56 151	72.76 212	85.01 272

VDDNet		80.50 133	78.26 154	87.21 38	86.19 220	69.79 37	94.48 47	91.31 164	60.42 295	79.34 100	90.91 144	38.48 289	96.56 112	82.16 82	81.05 158	95.27 60

ACMP	71.68 10	75.58 219	74.23 212	79.62 245	84.97 240	59.64 263	90.80 194	89.07 249	70.39 214	62.95 272	87.30 194	38.28 290	93.87 211	72.89 146	71.45 223	85.36 268
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		79.87 145	78.68 148	83.45 157	89.96 145	61.51 234	92.13 133	90.79 184	76.83 85	78.85 111	86.33 203	38.16 291	96.17 120	67.93 192	87.17 111	92.67 148
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
anonymousdsp		71.14 255	69.37 257	76.45 282	72.95 334	54.71 307	84.19 280	88.88 254	61.92 287	62.15 278	79.77 283	38.14 292	91.44 279	68.90 186	67.45 249	83.21 291

xiu_mvs_v1_base_debu		82.16 107	81.12 110	85.26 107	86.42 215	68.72 58	92.59 123	90.44 194	73.12 143	84.20 55	94.36 77	38.04 293	95.73 138	84.12 67	86.81 113	91.33 174

xiu_mvs_v1_base		82.16 107	81.12 110	85.26 107	86.42 215	68.72 58	92.59 123	90.44 194	73.12 143	84.20 55	94.36 77	38.04 293	95.73 138	84.12 67	86.81 113	91.33 174

xiu_mvs_v1_base_debi		82.16 107	81.12 110	85.26 107	86.42 215	68.72 58	92.59 123	90.44 194	73.12 143	84.20 55	94.36 77	38.04 293	95.73 138	84.12 67	86.81 113	91.33 174

PVSNet_0	68.08 15	71.81 251	68.32 263	82.27 178	84.68 242	62.31 224	88.68 244	90.31 200	75.84 94	57.93 301	80.65 272	37.85 296	94.19 194	69.94 174	29.05 353	90.31 189

Anonymous20231206		67.53 283	65.78 273	72.79 307	74.95 329	47.59 338	88.23 251	87.32 284	61.75 290	58.07 298	77.29 300	37.79 297	87.29 317	42.91 316	63.71 276	83.48 286

ACMM	69.62 13	74.34 229	72.73 230	79.17 252	84.25 253	57.87 282	90.36 206	89.93 216	63.17 275	65.64 248	86.04 207	37.79 297	94.10 196	65.89 213	71.52 222	85.55 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		78.18 177	75.77 193	85.41 102	87.14 206	69.11 48	92.96 106	91.15 172	66.71 250	70.47 190	86.07 205	37.49 299	96.48 114	70.15 173	79.80 163	90.65 185

LS3D		69.17 267	66.40 270	77.50 269	91.92 102	56.12 299	85.12 275	80.37 328	46.96 340	56.50 306	87.51 191	37.25 300	93.71 214	32.52 348	79.40 165	82.68 300

MDA-MVSNet_test_wron		63.78 301	60.16 304	74.64 292	78.15 315	60.41 253	83.49 285	84.03 311	56.17 320	39.17 349	71.59 330	37.22 301	83.24 337	42.87 318	48.73 333	80.26 322

YYNet1		63.76 302	60.14 305	74.62 293	78.06 316	60.19 258	83.46 287	83.99 315	56.18 319	39.25 348	71.56 331	37.18 302	83.34 335	42.90 317	48.70 334	80.32 321

FMVSNet5		68.04 278	65.66 276	75.18 290	84.43 249	57.89 281	83.54 284	86.26 292	61.83 289	53.64 314	73.30 321	37.15 303	85.08 324	48.99 290	61.77 291	82.56 302

test20.03		63.83 300	62.65 296	67.38 327	70.58 342	39.94 350	86.57 271	84.17 310	63.29 273	51.86 319	77.30 299	37.09 304	82.47 340	38.87 333	54.13 324	79.73 325

PVSNet	73.49 8	80.05 141	78.63 149	84.31 135	90.92 129	64.97 162	92.47 126	91.05 178	79.18 52	72.43 172	90.51 151	37.05 305	94.06 200	68.06 190	86.00 123	93.90 117

EU-MVSNet		64.01 299	63.01 293	67.02 328	74.40 331	38.86 354	83.27 289	86.19 293	45.11 344	54.27 310	81.15 266	36.91 306	80.01 347	48.79 292	57.02 314	82.19 307

Anonymous20231211		73.08 239	70.39 250	81.13 210	90.62 135	63.33 202	91.40 167	90.06 213	51.84 328	64.46 260	80.67 271	36.49 307	94.07 199	63.83 230	64.17 272	85.98 255

FMVSNet1		72.71 247	69.91 254	81.10 212	83.60 261	65.11 156	90.01 216	90.32 197	63.92 268	63.56 267	80.25 278	36.35 308	91.54 274	54.46 272	66.75 252	86.64 237

Anonymous20240529		76.84 197	74.15 214	84.88 117	91.02 126	64.95 163	93.84 76	91.09 175	53.57 324	73.00 159	87.42 192	35.91 309	97.32 71	69.14 183	72.41 217	92.36 155

CMPMVS	48.56 21	66.77 287	64.41 286	73.84 299	70.65 341	50.31 327	77.79 325	85.73 298	45.54 343	44.76 341	82.14 246	35.40 310	90.14 292	63.18 234	74.54 200	81.07 313
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		67.57 282	64.76 281	76.00 286	72.82 336	53.37 312	88.71 243	86.78 288	53.19 325	57.58 303	78.03 295	35.33 311	92.41 253	55.56 269	54.88 322	82.21 306

PatchMatch-RL		72.06 250	69.98 251	78.28 261	89.51 156	55.70 302	83.49 285	83.39 319	61.24 291	63.72 266	82.76 238	34.77 312	93.03 226	53.37 278	77.59 181	86.12 252

LTVRE_ROB	59.60 19	66.27 289	63.54 290	74.45 294	84.00 256	51.55 320	67.08 344	83.53 316	58.78 306	54.94 308	80.31 276	34.54 313	93.23 223	40.64 327	68.03 243	78.58 334
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
UniMVSNet_ETH3D		72.74 246	70.53 249	79.36 249	78.62 312	56.64 296	85.01 276	89.20 240	63.77 270	64.84 255	84.44 223	34.05 314	91.86 267	63.94 229	70.89 227	89.57 198

F-COLMAP		70.66 256	68.44 261	77.32 273	86.37 218	55.91 300	88.00 253	86.32 290	56.94 315	57.28 304	88.07 183	33.58 315	92.49 250	51.02 282	68.37 241	83.55 283

pmmvs-eth3d		65.53 293	62.32 298	75.19 289	69.39 344	59.59 264	82.80 294	83.43 317	62.52 282	51.30 323	72.49 322	32.86 316	87.16 318	55.32 270	50.73 330	78.83 332

MDA-MVSNet-bldmvs		61.54 308	57.70 312	73.05 304	79.53 297	57.00 295	83.08 292	81.23 324	57.57 309	34.91 351	72.45 323	32.79 317	86.26 322	35.81 338	41.95 342	75.89 340

MIMVSNet		71.64 252	68.44 261	81.23 206	81.97 275	64.44 173	73.05 331	88.80 257	69.67 222	64.59 256	74.79 317	32.79 317	87.82 310	53.99 274	76.35 195	91.42 172

UnsupCasMVSNet_eth		65.79 292	63.10 292	73.88 298	70.71 340	50.29 328	81.09 304	89.88 218	72.58 154	49.25 330	74.77 318	32.57 319	87.43 316	55.96 268	41.04 344	83.90 281

N_pmnet		50.55 319	49.11 322	54.88 334	77.17 321	4.02 367	84.36 279	2.00 366	48.59 336	45.86 338	68.82 335	32.22 320	82.80 339	31.58 350	51.38 329	77.81 336

test_0402		64.54 296	61.09 302	74.92 291	84.10 255	60.75 247	87.95 254	79.71 330	52.03 327	52.41 317	77.20 301	32.21 321	91.64 271	23.14 352	61.03 298	72.36 344

DSMNet-mixed		56.78 315	54.44 318	63.79 330	63.21 350	29.44 358	64.43 346	64.10 354	42.12 348	51.32 322	71.60 329	31.76 322	75.04 349	36.23 336	65.20 262	86.87 234

MSDG		69.54 265	65.73 274	80.96 217	85.11 238	63.71 194	84.19 280	83.28 320	56.95 314	54.50 309	84.03 225	31.50 323	96.03 129	42.87 318	69.13 236	83.14 293

RPSCF		64.24 298	61.98 300	71.01 318	76.10 327	45.00 342	75.83 328	75.94 335	46.94 341	58.96 293	84.59 221	31.40 324	82.00 344	47.76 299	60.33 306	86.04 253

jajsoiax		73.05 240	71.51 244	77.67 267	77.46 319	54.83 306	88.81 242	90.04 214	69.13 230	62.85 274	83.51 231	31.16 325	92.75 239	70.83 166	69.80 229	85.43 267

MVS-HIRNet		60.25 311	55.55 317	74.35 295	84.37 250	56.57 297	71.64 333	74.11 340	34.44 351	45.54 340	42.24 353	31.11 326	89.81 294	40.36 328	76.10 196	76.67 339

SixPastTwentyTwo		64.92 294	61.78 301	74.34 296	78.74 309	49.76 329	83.42 288	79.51 331	62.86 277	50.27 326	77.35 298	30.92 327	90.49 284	45.89 306	47.06 336	82.78 294

DIV-MVS_2432*1600		60.87 309	58.60 309	67.68 325	66.13 347	39.93 351	75.63 329	84.70 306	57.32 312	49.57 329	68.45 336	29.55 328	82.87 338	48.09 294	47.94 335	80.25 323

mvs_tets		72.71 247	71.11 245	77.52 268	77.41 320	54.52 308	88.45 249	89.76 220	68.76 235	62.70 275	83.26 235	29.49 329	92.71 240	70.51 172	69.62 231	85.34 269

Anonymous202405211		77.96 181	75.33 197	85.87 84	93.73 55	64.52 168	94.85 43	85.36 300	62.52 282	76.11 134	90.18 158	29.43 330	97.29 73	68.51 188	77.24 190	95.81 40

K. test v3		63.09 303	59.61 307	73.53 301	76.26 326	49.38 332	83.27 289	77.15 333	64.35 265	47.77 334	72.32 326	28.73 331	87.79 311	49.93 287	36.69 348	83.41 288

UnsupCasMVSNet_bld		61.60 307	57.71 311	73.29 303	68.73 345	51.64 319	78.61 319	89.05 250	57.20 313	46.11 335	61.96 344	28.70 332	88.60 302	50.08 286	38.90 346	79.63 326

lessismore_v0				73.72 300	72.93 335	47.83 337		61.72 357		45.86 338	73.76 319	28.63 333	89.81 294	47.75 300	31.37 352	83.53 284

new-patchmatchnet		59.30 314	56.48 315	67.79 324	65.86 348	44.19 343	82.47 295	81.77 323	59.94 300	43.65 345	66.20 339	27.67 334	81.68 345	39.34 330	41.40 343	77.50 337

ACMH	63.93 17	68.62 272	64.81 280	80.03 233	85.22 234	63.25 203	87.72 258	84.66 307	60.83 293	51.57 321	79.43 287	27.29 335	94.96 166	41.76 321	64.84 265	81.88 308
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		64.68 295	62.17 299	72.21 312	76.08 328	47.35 339	80.67 306	81.02 325	56.19 318	51.60 320	79.66 285	27.05 336	88.56 303	53.60 277	53.63 325	80.71 317

ACMH+	65.35 16	67.65 281	64.55 283	76.96 279	84.59 245	57.10 294	88.08 252	80.79 326	58.59 308	53.00 315	81.09 267	26.63 337	92.95 228	46.51 302	61.69 295	80.82 315

OpenMVS_ROB	61.12 18	66.39 288	62.92 294	76.80 281	76.51 324	57.77 283	89.22 233	83.41 318	55.48 321	53.86 313	77.84 296	26.28 338	93.95 208	34.90 341	68.76 238	78.68 333

MVS_0304		68.99 271	67.23 268	74.28 297	80.36 288	52.54 315	87.01 267	86.36 289	59.89 301	66.22 245	73.56 320	24.25 339	88.03 308	57.34 264	70.11 228	82.27 305

COLMAP_ROB	57.96 20	62.98 304	59.65 306	72.98 305	81.44 278	53.00 314	83.75 283	75.53 338	48.34 338	48.81 331	81.40 259	24.14 340	90.30 285	32.95 345	60.52 303	75.65 341
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		60.16 312	57.33 313	68.67 322	69.71 343	44.13 344	78.92 318	84.21 309	55.05 322	44.63 342	71.85 328	23.91 341	81.54 346	32.63 347	55.03 321	80.35 320

testgi		64.48 297	62.87 295	69.31 321	71.24 337	40.62 349	85.49 274	79.92 329	65.36 260	54.18 311	83.49 232	23.74 342	84.55 326	41.60 322	60.79 301	82.77 295

ITE_SJBPF				70.43 319	74.44 330	47.06 340		77.32 332	60.16 298	54.04 312	83.53 230	23.30 343	84.01 329	43.07 315	61.58 296	80.21 324

EG-PatchMatch MVS		68.55 273	65.41 278	77.96 265	78.69 310	62.93 211	89.86 221	89.17 242	60.55 294	50.27 326	77.73 297	22.60 344	94.06 200	47.18 301	72.65 214	76.88 338

tmp_tt		22.26 330	23.75 332	17.80 344	5.23 366	12.06 366	35.26 356	39.48 361	2.82 362	18.94 355	44.20 352	22.23 345	24.64 362	36.30 335	9.31 359	16.69 356

USDC		67.43 285	64.51 284	76.19 284	77.94 317	55.29 304	78.38 321	85.00 303	73.17 141	48.36 332	80.37 275	21.23 346	92.48 251	52.15 280	64.02 274	80.81 316

Anonymous20240521		62.09 305	59.08 308	71.10 317	67.19 346	48.72 334	83.91 282	85.23 301	50.38 332	47.84 333	71.22 332	20.74 347	85.51 323	46.47 303	58.75 311	79.06 330

XVG-ACMP-BASELINE		68.04 278	65.53 277	75.56 287	74.06 332	52.37 316	78.43 320	85.88 296	62.03 285	58.91 294	81.21 265	20.38 348	91.15 280	60.69 249	68.18 242	83.16 292

AllTest		61.66 306	58.06 310	72.46 309	79.57 295	51.42 322	80.17 312	68.61 349	51.25 329	45.88 336	81.23 261	19.86 349	86.58 320	38.98 331	57.01 315	79.39 327

TestCases				72.46 309	79.57 295	51.42 322		68.61 349	51.25 329	45.88 336	81.23 261	19.86 349	86.58 320	38.98 331	57.01 315	79.39 327

pmmvs3		55.51 316	51.50 321	67.53 326	57.90 355	50.93 325	80.37 308	73.66 341	40.63 349	44.15 344	64.75 342	16.30 351	78.97 348	44.77 312	40.98 345	72.69 343

TDRefinement		55.28 317	51.58 320	66.39 329	59.53 354	46.15 341	76.23 327	72.80 342	44.60 345	42.49 346	76.28 310	15.29 352	82.39 341	33.20 344	43.75 340	70.62 346

new_pmnet		49.31 320	46.44 323	57.93 332	62.84 351	40.74 348	68.47 340	62.96 356	36.48 350	35.09 350	57.81 346	14.97 353	72.18 350	32.86 346	46.44 337	60.88 350

TinyColmap		60.32 310	56.42 316	72.00 315	78.78 308	53.18 313	78.36 322	75.64 336	52.30 326	41.59 347	75.82 314	14.76 354	88.35 305	35.84 337	54.71 323	74.46 342

LF4IMVS		54.01 318	52.12 319	59.69 331	62.41 352	39.91 352	68.59 339	68.28 351	42.96 347	44.55 343	75.18 315	14.09 355	68.39 352	41.36 324	51.68 328	70.78 345

PM-MVS		59.40 313	56.59 314	67.84 323	63.63 349	41.86 346	76.76 326	63.22 355	59.01 305	51.07 324	72.27 327	11.72 356	83.25 336	61.34 245	50.28 332	78.39 335

ambc				69.61 320	61.38 353	41.35 347	49.07 355	85.86 297		50.18 328	66.40 338	10.16 357	88.14 307	45.73 307	44.20 339	79.32 329

FPMVS		45.64 321	43.10 324	53.23 336	51.42 357	36.46 355	64.97 345	71.91 344	29.13 353	27.53 352	61.55 345	9.83 358	65.01 356	16.00 355	55.58 319	58.22 351

ANet_high		40.27 323	35.20 326	55.47 333	34.74 363	34.47 356	63.84 347	71.56 345	48.42 337	18.80 356	41.08 354	9.52 359	64.45 357	20.18 353	8.66 360	67.49 348

EMVS		23.76 329	23.20 333	25.46 343	41.52 361	16.90 365	60.56 349	38.79 363	14.62 359	8.99 362	20.24 361	7.35 360	45.82 360	7.25 360	9.46 358	13.64 358

E-PMN		24.61 327	24.00 331	26.45 342	43.74 360	18.44 364	60.86 348	39.66 360	15.11 358	9.53 361	22.10 358	6.52 361	46.94 359	8.31 359	10.14 357	13.98 357

DeepMVS_CX				34.71 341	51.45 356	24.73 362		28.48 365	31.46 352	17.49 357	52.75 347	5.80 362	42.60 361	18.18 354	19.42 354	36.81 353

Gipuma		34.91 325	31.44 328	45.30 338	70.99 339	39.64 353	19.85 359	72.56 343	20.10 357	16.16 358	21.47 359	5.08 363	71.16 351	13.07 356	43.70 341	25.08 355
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		40.54 322	35.79 325	54.76 335	36.92 362	30.81 357	51.41 353	69.02 348	22.07 355	24.63 353	45.37 351	4.56 364	65.81 354	33.67 342	34.50 351	67.67 347

PMMVS2		37.93 324	33.61 327	50.92 337	46.31 359	24.76 361	60.55 350	50.05 358	28.94 354	20.93 354	47.59 348	4.41 365	65.13 355	25.14 351	18.55 355	62.87 349

PMVS	26.43 22	31.84 326	28.16 329	42.89 339	25.87 365	27.58 359	50.92 354	49.78 359	21.37 356	14.17 359	40.81 355	2.01 366	66.62 353	9.61 358	38.88 347	34.49 354
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 328	19.77 334	38.09 340	34.56 364	26.92 360	26.57 357	38.87 362	11.73 360	11.37 360	27.44 356	1.37 367	50.42 358	11.41 357	14.60 356	36.93 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 332	10.95 335	12.33 345	48.05 358	19.89 363	25.89 358	1.92 367	3.58 361	3.12 363	1.37 363	0.64 368	15.77 363	6.23 361	7.77 361	1.35 359

test123		6.92 335	9.21 338	0.08 346	0.03 368	0.05 368	81.65 300	0.01 369	0.02 364	0.14 365	0.85 365	0.03 369	0.02 364	0.12 363	0.00 363	0.16 360

testmvs		7.23 334	9.62 337	0.06 347	0.04 367	0.02 369	84.98 277	0.02 368	0.03 363	0.18 364	1.21 364	0.01 370	0.02 364	0.14 362	0.01 362	0.13 361

uanet_test		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 363	0.00 362

sosnet-low-res		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 363	0.00 362

sosnet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 363	0.00 362

uncertanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 363	0.00 362

Regformer		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 363	0.00 362

ab-mvs-re		7.91 333	10.55 336	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	94.95 58	0.00 371	0.00 366	0.00 364	0.00 363	0.00 362

uanet		0.00 337	0.00 340	0.00 348	0.00 369	0.00 370	0.00 360	0.00 370	0.00 365	0.00 366	0.00 366	0.00 371	0.00 366	0.00 364	0.00 363	0.00 362

IU-MVS					96.46 10	69.91 35		95.18 13	80.75 35	95.28 1				92.34 6	95.36 12	96.47 20

save fliter					93.84 49	67.89 78	95.05 38	92.66 112	78.19 66

test_0728_SECOND				88.70 13	96.45 11	70.43 26	96.64 8	94.37 47					99.15 2	91.91 10	94.90 19	96.51 18

GSMVS																94.68 81

test_part2					96.29 17	68.16 72				90.78 10

MTGPA								92.23 126

MTMP							93.77 78	32.52 364

gm-plane-assit					88.42 179	67.04 104			78.62 63		91.83 134		97.37 67	76.57 125

test9_res														89.41 20	94.96 16	95.29 56

agg_prior2														86.41 51	94.75 26	95.33 51

agg_prior					94.16 44	66.97 106		93.31 85		84.49 52			96.75 106

test_prior4						67.18 100	93.92 69

test_prior				86.42 66	94.71 33	67.35 93		93.10 97					96.84 102			95.05 70

旧先验2							92.00 143		59.37 304	87.54 25			93.47 220	75.39 131

新几何2							91.41 165

无先验							92.71 115	92.61 116	62.03 285				97.01 88	66.63 202		93.97 112

原ACMM2							92.01 141

testdata2													96.09 123	61.26 246

testdata1							89.21 234		77.55 76

plane_prior7					86.94 208	61.51 234

plane_prior5								91.31 164					95.55 152	76.74 123	78.53 175	88.39 212

plane_prior4											89.14 169

plane_prior3						61.95 228			79.09 55	72.53 168

plane_prior2							93.13 98		78.81 60

plane_prior1					87.15 205

plane_prior						62.42 220	93.85 73		79.38 48						78.80 172

n2								0.00 370

nn								0.00 370

door-mid								66.01 353

test11								93.01 99

door								66.57 352

HQP5-MVS						63.66 197

HQP-NCC					87.54 197		94.06 60		79.80 42	74.18 149

ACMP_Plane					87.54 197		94.06 60		79.80 42	74.18 149

BP-MVS														77.63 120

HQP4-MVS										74.18 149			95.61 146			88.63 206

HQP3-MVS								91.70 150							78.90 170

NP-MVS					87.41 200	63.04 207					90.30 156

ACMMP++_ref															71.63 220

ACMMP++															69.72 230

High-resolution multi-view benchmark